Search results for: Deep learning based segmentation

Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

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Authors: Mabel Ortiz, Claudio Díaz

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This paper explores Chilean pre-service teachers' perceptions about the provision of corrective feedback in a wiki environment during the collaborative writing of an argumentative essay. After conducting a semi-structured interview on 22 participants, the data were processed through the content analysis technique. The results show that students have positive perceptions about corrective feedback, provided through a wiki virtual environment, which in turn facilitates feedback provision and impacts language learning effectively. Some of the positive perceptions about virtual feedback refer to permanent access, efficiency, simultaneous revision and immediacy. It would then be advisable to integrate wiki-based feedback as a methodology for the language classroom and collaborative writing tasks.

Keywords: argumentative essay, focused corrective feedback, perception, wiki environment

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: Christina Killian, Katie Wall, Séamus Fanning, Guerrino Macori

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Deep-level metagenomics offers a useful technical approach to explore the three dynamic One Health axes: healthcare, domiciliary and veterinary. There is currently limited understanding of the composition of complex biofilms, natural abundance of AMR genes and gene transfer occurrence in these ecological niches. By using a newly established small-scale complex biofilm model, COMBAT has the potential to provide new information on microbial diversity, antimicrobial resistance (AMR)-encoding gene abundance, and their transfer in complex biofilms of importance to these three One Health axes. Shotgun metagenomics has been used to sample the genomes of all microbes comprising the complex communities found in each biofilm source. A comparative analysis between untreated and biocide-treated biofilms is described. The basic steps include the purification of genomic DNA, followed by library preparation, sequencing, and finally, data analysis. The use of long-read sequencing facilitates the completion of metagenome-assembled genomes (MAG). Samples were sequenced using a PromethION platform, and following quality checks, binning methods, and bespoke bioinformatics pipelines, we describe the recovery of individual MAGs to identify mobile gene elements (MGE) and the corresponding AMR genotypes that map to these structures. High-throughput sequencing strategies have been deployed to characterize these communities. Accurately defining the profiles of these niches is an essential step towards elucidating the impact of the microbiota on each niche biofilm environment and their evolution.

Keywords: COMBAT, biofilm, metagenomics, high-throughput sequencing

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Authors: Magdi. M. Nabi, Ding-Li Yu

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Control of a semi-batch polymerization reactor using an adaptive radial basis function (RBF) neural network method is investigated in this paper. A neural network inverse model is used to estimate the valve position of the reactor; this method can identify the controlled system with the RBF neural network identifier. The weights of the adaptive PID controller are timely adjusted based on the identification of the plant and self-learning capability of RBFNN. A PID controller is used in the feedback control to regulate the actual temperature by compensating the neural network inverse model output. Simulation results show that the proposed control has strong adaptability, robustness and satisfactory control performance and the nonlinear system is achieved.

Keywords: Chylla-Haase polymerization reactor, RBF neural networks, feed-forward, feedback control

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Authors: Jonathan D. Moore, Mark G. Reith, David S. Long

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Serious games (SGs) have been shown to be an effective teaching tool in many contexts. Because of the success of SGs, several design frameworks have been created to expedite the process of making original serious games to teach specific learning objectives (LOs). Even with these frameworks, the time required to create a custom SG from conception to implementation can range from months to years. Furthermore, it is even more difficult to design a game framework that allows an instructor to create customized game variants supporting multiple LOs within the same field. This paper proposes a refactoring methodology to apply the theoretical principles from well-established design frameworks to a pre-existing serious game. The expected result is a generalized game that can be quickly customized to teach LOs not originally targeted by the game. This methodology begins by describing the general components in a game, then uses a combination of two SG design frameworks to extract the teaching elements present in the game. The identified teaching elements are then used as the theoretical basis to determine the range of LOs that can be taught by the game. This paper evaluates the proposed methodology by presenting a case study of refactoring the serious game Battlespace Next (BSN) to teach joint military capabilities. The range of LOs that can be taught by the generalized BSN are identified, and examples of creating custom LOs are given. Survey results from users of the generalized game are also provided. Lastly, the expected impact of this work is discussed and a road map for future work and evaluation is presented.

Keywords: serious games, learning objectives, game design, learning theory, game framework

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Authors: Alejandra Torres-Landa López

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Introduction: Can architecture students be creative in spaces conformed by an educational infrastructure build with paradigms of the past?, this question and others related are answered in this paper as it presents the PhD research: An anthropic conflict in Mexican Higher Education Institutes, problems and challenges of the educational infrastructure in teaching and learning History of Architecture. This research was finished in 2013 and is one of the first studies conducted nationwide in Mexico that analysis the educational infrastructure impact in learning architecture; its objective was to identify which elements of the educational infrastructure of Mexican Higher Education Institutes where architects are formed, hinder or contribute to the teaching and learning of History of Architecture; how and why it happens. The methodology: A mixed methodology was used combining quantitative and qualitative analysis. Different resources and strategies for data collection were used, such as questionnaires for students and teachers, interviews to architecture research experts, direct observations in Architecture classes, among others; the data collected was analyses using SPSS and MAXQDA. The veracity of the quantitative data was supported by the Cronbach’s Alpha Coefficient, obtaining a 0.86, figure that gives the data enough support. All the above enabled to certify the anthropic conflict in which Mexican Universities are. Major findings of the study: Although some of findings were probably not unknown, they haven’t been systematized and analyzed with the depth to which it’s done in this research. So, it can be said, that the educational infrastructure of most of the Higher Education Institutes studied, is a barrier to the educational process, some of the reasons are: the little morphological variation of space, the inadequate control of lighting, noise, temperature, equipment and furniture, the poor or none accessibility for disable people; as well as the absence, obsolescence and / or insufficiency of information technologies are some of the issues that generate an anthropic conflict understanding it as the trouble that teachers and students have to relate between them, in order to achieve significant learning). It is clear that most of the educational infrastructure of Mexican Higher Education Institutes is anchored to paradigms of the past; it seems that they respond to the previous era of industrialization. The results confirm that the educational infrastructure of Mexican Higher Education Institutes where architects are formed, is perceived as a "closed container" of people and data; infrastructure that becomes a barrier to teaching and learning process. Conclusion: The research results show it's time to change the paradigm in which we conceive the educational infrastructure, it’s time to stop seen it just only as classrooms, workshops, laboratories and libraries, as it must be seen from a constructive, urban, architectural and human point of view, taking into account their different dimensions: physical, technological, documental, social, among others; so the educational infrastructure can become a set of elements that organize and create spaces where ideas and thoughts can be shared; to be a social catalyst where people can interact between each other and with the space itself.

Keywords: educational infrastructure, impact of space in learning architecture outcomes, learning environments, teaching architecture, learning architecture

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Authors: Kadir Özsoy

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Creating a moderately competitive learning environment is believed to have positive effects on student interest and motivation. The best way today to attract young learners to get involved in a fun, competitive learning experience is possible through mobile applications as these learners mostly rely on games and applications on their phones and tablets to have fun, communicate, look for information and study. In this study, a mobile application called ‘QuizUp’ is used to create a specific game topic for elementary level students at Anadolu University Preparatory School. The topic is specially designed with weekly-added questions in accordance with the course syllabus. Students challenge their classmates or randomly chosen opponents to answer questions related to their course subjects. They also chat and post on the topic’s wall in English. The study aims at finding out students’ perceptions towards the use of the application as a classroom and extra-curricular activity through a survey. The study concludes that educational games boost students’ motivation, lead to increased effort, and positively change their studying habits.

Keywords: competitive learning, educational application, effort, motivation 'QuizUp', study habits

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Authors: Atiya Khan

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Recent research on the condition of school education in India has reaffirmed the importance of quality teacher professional development, especially in light of the rapid changes in teaching methods, learning theories, curriculum, and major shifts in information and technology that education systems are experiencing around the world. However, the quality of programs of teacher professional development in India is often uneven, in some cases non-existing. The educational authorities in India have long recognized this and have developed a range of programs to assist in-service teacher education. But, these programs have been mostly inadequate at improving the quality of teachers in India. Policy literature and reports indicate that the unevenness of these programs and more generally the lack of quality teacher professional development in India are due to factors such as a large number of teachers, budgetary constraints, top-down decision making, teacher overload, lack of infrastructure, and little or no follow-up. The disparity between the government stated goals for quality teacher professional development in India and its inability to meet the learning needs of teachers suggests that new interventions are needed. The realization that globalization has brought about an increase in the social, cultural, political and economic interconnectedness between countries has also given rise to transnational opportunities for education systems, such as India’s, aiming to build their capacity to support teacher professional development. Moreover, new developments in communication technologies seem to present a plausible means of achieving high-quality professional development for teachers through the creation of social learning spaces, such as transnational learning networks. This case study investigates the potential of one such transnational learning network to support the quality of teacher professional development in India, namely the Australia-Asia BRIDGE School Partnerships Project. It explores the participation of some fifteen teachers and their principals from BRIDGE participating schools in Delhi region of India; focusing on their professional development expectations from the BRIDGE program and account for their experiences in the program, in order to determine the program’s potential for the professional development of teachers in this study.

Keywords: case study, Australia-Asia BRIDGE Project, teacher professional development, transnational learning networks

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Authors: Mzamani Aaron Mabasa

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The research study examines the reality that the Language Services Directorate can be considered a language repository hub. The study postulates that multilingual education guided by language policy implementation can improve student performance and pass rate. Various documents in the form of style guides, glossaries and tutorial letters may be used to enable students to understand complex words, sentences, phrases and paragraphs when technical vocabularies are used. This paper addresses the way in which quality assurance can transform South African official languages, including Sign Language, as mandated by the Language Policy for Higher Education. The paper further emphasizes that Language Services is unique in the sense that it involves all South African officials as tools for student support and facilitation of learning. This is in line with the Constitution of the Republic of South Africa (1996) and the Unisa Language Policy of 2023, which declares the status, parity and esteem of these official languages regarding usage in formal function domains, namely education, economy, social and politics. The aim of this paper is to ensure that quality assurance is ultimately accomplished in terms of teaching and learning standards. Eventually, all South African languages can be used for official domains to achieve functional multilingualism. This paper furthermore points out that content analysis as a research instrument as far as a qualitative approach is concerned may be used as a data collection technique.

Keywords: repository, multilingualism, policy, education

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Authors: Nirmani Wijenayake, Louise Lutze-Mann, Lucy Jo, John Wilson, Vivian Yeung, Dean Lovett, Kim Snepvangers

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Sessional academic staff at universities have the most influence and impact on student learning, engagement, and experience as they have the most direct contact with undergraduate students. A blended technology-enhanced program was created for the development and support of sessional staff to ensure adequate training is provided to deliver quality educational outcomes for the students. This program combines innovative mixed media educational modules, a peer-driven support forum, and face-to-face workshops to provide a comprehensive training and support package for staff. Additionally, the program encourages the development of learning communities and peer mentoring among the sessional staff to enhance their support system. In 2018, the program was piloted on 100 sessional staff in the School of Biotechnology and Biomolecular Sciences to evaluate the effectiveness of this model. As part of the program, rotoscope animations were developed to showcase ‘typical’ interactions between staff and students. These were designed around communication, confidence building, consistency in grading, feedback, diversity awareness, and mental health and wellbeing. When surveyed, 86% of sessional staff found these animations to be helpful in their teaching. An online platform (Moodle) was set up to disseminate educational resources and teaching tips, to host a discussion forum for peer-to-peer communication and to increase critical thinking and problem-solving skills through scenario-based lessons. The learning analytics from these lessons were essential in identifying difficulties faced by sessional staff to further develop supporting workshops to improve outcomes related to teaching. The face-to-face professional development workshops were run by expert guest speakers on topics such as cultural diversity, stress and anxiety, LGBTIQ and student engagement. All the attendees of the workshops found them to be useful and 88% said they felt these workshops increase interaction with their peers and built a sense of community. The final component of the program was to use an adaptive e-learning platform to gather feedback from the students on sessional staff teaching twice during the semester. The initial feedback provides sessional staff with enough time to reflect on their teaching and adjust their performance if necessary, to improve the student experience. The feedback from students and the sessional staff on this model has been extremely positive. The training equips the sessional staff with knowledge and insights which can provide students with an exceptional learning environment. This program is designed in a flexible and scalable manner so that other faculties or institutions could adapt components for their own training. It is anticipated that the training and support would help to build the next generation of educators who will directly impact the educational experience of students.

Keywords: designing effective instruction, enhancing student learning, implementing effective strategies, professional development

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Mohammad Ghiasvand, Babak Khorsandi, Morteza Kolahdoozan

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Under the influence of waves, oil in the sea is subject to vertical scattering in the water column. Scientists' knowledge of how oil is dispersed in the water column is one of the lowest levels of knowledge among other processes affecting oil in the marine environment, which highlights the need for research and study in this field. Therefore, this study investigates the distribution of oil in the water column in a turbulent environment with zero velocity characteristics. Lack of laboratory results to analyze the distribution of petroleum pollutants in deep water for information Phenomenon physics on the one hand and using them to calibrate numerical models on the other hand led to the development of laboratory models in research. According to the aim of the present study, which is to investigate the distribution of oil in homogeneous and isotropic turbulence caused by the oscillating Grid, after reaching the ideal conditions, the crude oil flow was poured onto the water surface and oil was distributed in deep water due to turbulence was investigated. In this study, all experimental processes have been implemented and used for the first time in Iran, and the study of oil diffusion in the water column was considered one of the key aspects of pollutant diffusion in the oscillating Grid environment. Finally, the required oscillation velocities were taken at depths of 10, 15, 20, and 25 cm from the water surface and used in the analysis of oil diffusion due to turbulence parameters. The results showed that with the characteristics of the present system in two static modes and network motion with a frequency of 0.8 Hz, the results of oil diffusion in the four mentioned depths at a frequency of 0.8 Hz compared to the static mode from top to bottom at 26.18, 57 31.5, 37.5 and 50% more. Also, after 2.5 minutes of the oil spill at a frequency of 0.8 Hz, oil distribution at the mentioned depths increased by 49, 61.5, 85, and 146.1%, respectively, compared to the base (static) state.

Keywords: homogeneous and isotropic turbulence, oil distribution, oscillating grid, oil spill

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Don August G. Delgado

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Foreign Language acquisition without enough motivation is tough because it is the force that drives students’ interest or enthusiasm to achieve learning. In addition, it also serves as the students’ beacon to achieve their goals, desires, dreams, and aspirations in life. Since it plays an integral factor in language learning acquisition, this study focuses on the integrative and instrumental motivation levels of all the first year students of Naresuan University International College. The identification of their motivation level and inclination in learning the English language will greatly help all NUIC lecturers and administrators to create a project or activities that they will truly enjoy and find worth doing. However, if the findings of this study will say otherwise, this study can also show to NUIC lecturers and administrators how they can help and transform NUIC freshmen on becoming motivated learners to enhance their English proficiency levels. All respondents in this study received an adopted and developed questionnaire from different researches in the same perspective. The questionnaire has 24 questions that were randomly arranged; 12 for integrative motivation and 12 for instrumental motivation. The questionnaire employed the five-point Likert scale. The tabulated data were analyzed according to its means and standard deviations using the Standard Deviation Calculator. In order to interpret the motivation level of the respondents, the Interpretation of Mean Scores was utilized. Thus, this study concludes that majority of the NUIC freshmen are neither integratively motivated nor instrumentally motivated students.

Keywords: motivation, integrative, foreign language acquisition, instrumental

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Authors: Ana Martins, Isabel Martins

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The purpose of this research is to ascertain whether students at HEIs cultivate distributed leadership and higher-level skills to inspire knowledge creation. Critical reflection of extant literature illustrates the need for a culture of innovation in organizational sustainability. New age leadership behaviors harmonize innovation. The leadership self-efficacy construct supports organizational learning. This exploratory study applies the pragmatic paradigm methodology using the survey research method for primary data collection. A questionnaire was distributed to a sample of university students based in the Southern Anatolian region of Turkey, from both under and postgraduate Business degree programs. An analysis of the findings reveals a greater connection in influencing behavior relying more on the task-centered perspective rather than with the people perspective. These results reveal the need for HEIs to instill a humanistic perspective in curricula enabling graduates to be capable leaders with the awareness soft skills to energize creativity and innovation. A limitation of this research is that one university makes it difficult to generalize to a broader population. This study is of added value for scholars and organizations in the current knowledge and innovation economy.

Keywords: distributed leadership, exploration, higher education institutions, innovation, knowledge creation, learning, self-efficacy

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Authors: Fatima Ghassan Al-Abtah, Naser Al-Huniti, Elsadig Mahdi

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As the lightest constructional metal on earth, magnesium alloys offer excellent potential for weight reduction in the transportation industry, and it was observed that some magnesium alloys exhibit superior ductility and superplastic behavior at high temperatures. The main limitation of the superplastic forming (SPF) includes the low production rate since it needs a long forming time for each part. Through this study, an SPF process that starts with a mechanical pre-forming stage is developed to promote formability and reduce forming time. A two-dimensional finite element model is used to simulate the process. The forming process consists of two steps. At the pre-forming step (deep drawing), the sheet is drawn into the die to a preselected level, using a mechanical punch, and at the second step (SPF) a pressurized gas is applied at a controlled rate. It is shown that a significant reduction in forming time and improved final thickness uniformity can be achieved when the hybrid forming technique is used, where the process achieved a fully formed part at 400°C. Investigation for the impact of different forming process parameters achieved by comparing forming time and the distribution of final thickness that were obtained from the simulation analysis. Maximum thinning decreased from over 67% to less than 55% and forming time significantly decreased by more than 6 minutes, and the required gas pressure profile was predicted for optimum forming process parameters based on the 0.001/sec target constant strain rate within the sheet.

Keywords: magnesium, plasticity, superplastic forming, finite element analysis

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Authors: Ntandoni Gloria Biyela

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During traditional times, there were no formal institutions of learning as they are today, where children attend classes to acquire or develop knowledge. This does not mean that there was no learning in indigenous African societies. Grandparents used to tell their grandchildren stories or teach them educational games around the fireplace, which this study refers to as a ‘traditional classroom’. A story recreated in symbolic or allegorical way, forms a base for a society’s beliefs, customs, accepted norms and language learning. Through folklore narratives, a society develops its own self awareness and education. So narrative characters, especially animals may be mythical products of the pre-literate folklore world and thus show the closeness that the Zulu society had with the wildlife. Oral cultures strive to create new facets of meaning by the use of animal metaphors to reflect the relationship of humans with the animal realm and to contribute to the language learning or literature in cross-cultural studies. Although animal metaphors are widespread in Zulu language because of the Zulu nation’s traditional closeness to wildlife, little field-research has been conducted on the social behavior of animals on the way in which their characteristics were transferred with precision to depictions of King Shaka’s behavior and activities during the amalgamation of Nguni clans into a Zulu kingdom. This study attempts to fill the gap by using first-hand interviews with local informants in areas traditionally linked to the king in KwaZulu-Natal province, South Africa. Departing from the conceptual metaphor theory, the study concentrates on King Shaka’s praise-poems in which the praise-poet describes his physical and dispositional characteristics through bold animal metaphors of the ‘Big Five’; namely, the lion, the leopard, the buffalo, the rhinoceros and the elephant, which are often referred to as Zulu royal favorites. These metaphors are still learnt by young and old in the 21st century because they reflect the responsibilities, status, and integrity of the king and the respect in which he is held by his people. They also project the crescendo growth of the Zulu nation, which, through the fulfillment of his ambitions, grew from a small clan to a mighty kingdom.

Keywords: animal, indigenous, learning, metaphor

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Authors: Sheila Santharamohana, Susan Bennett

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Historically, the attrition rates in secondary schools of Indigenous people or Orang Asli of Malaysia have been a cause for nationwide concern. Efforts to increase student engagement focusing on curriculum re-design and aid have not had the targeted impact. The scope of the research explored a change model incorporating project-based learning and wrap-around support through school-community partnerships to increase Orang Asli engagement, student outcomes and improve cultural connectedness. The evaluation methodology was mixed-method comprising a student questionnaire, interviews, and document analysis. Data and evidence were gathered from school staff, community, the Orang Asli governmental authority (JAKOA) and external agencies. Findings from the year-long research suggests shared values and goals in school-community partnerships foster responsive leadership and is key to safeguarding vulnerable Orang Asli, resulting in improved student outcomes. The research highlighted the barriers to the recognition and distinct needs and unique values of the Orang Asli that impact their educational equity and outcomes.

Keywords: Indigenous Education, Cultural Connectedness, School-Community Partnership, Student Outcomes

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Authors: Obokhai Kess Asikhia

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This work explores design concept generation by understanding user needs through observation and interview. The aim is to examine several principles and guidelines in obtaining evidence from observing how users interact with the targeted product and interviewing them to acquire deep insights of their needs. With the help of Quality Function Deployment (QFD), the identified needs of the users while interacting with the product were ranked using the normalised weighting approach. Furthermore, a low fidelity prototype of the alarm clock is developed with a view of addressing the identified needs of the users. Finally, the low fidelity prototype design was evaluated with two design prototypes already existing in the market through a study involving 30 participants. Preliminary results reveal higher performance ratings by the majority of the participants of the new prototype compared to the other existing alarm clocks in the market used in the study.

Keywords: design concept, low fidelity prototype, normalised weighting approach, quality function deployment, user needs

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Troels Bo Jørgensen, Preben Hagh Strunge Holm, Henrik Gordon Petersen, Norbert Kruger

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This paper presents a simulation framework for using machine learning techniques to determine robust robotic motions for handling deformable objects. The main focus is on applications in the meat sector, which mainly handle three-dimensional objects. In order to optimize the robotic handling, the robot motions have been parameterized in terms of grasp points, robot trajectory and robot speed. The motions are evaluated based on a dynamic simulation environment for robotic control of deformable objects. The evaluation indicates certain parameter setups, which produce robust motions in the simulated environment, and based on a visual analysis indicate satisfactory solutions for a real world system.

Keywords: deformable objects, robotic manipulation, simulation, real world system

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Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

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Authors: Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, Ulrich Stimming, Alpha Lee

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Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS) -- a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis -- with Gaussian process machine learning. We collect over 20,000 EIS spectra of commercial Li-ion batteries at different states of health, states of charge and temperatures -- the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.

Keywords: battery degradation, machine learning method, electrochemical impedance spectroscopy, battery diagnosis

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Authors: Fernando Augusto Ullmann Tobe, Moacyr Amaral Domingues, Figueiredo, Stephany Rie Yamamoto Gushiken

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The purpose of this paper is to present the process of elaborating the layout of an assembly line of a welding industry using the principles of lean manufacturing as the main driver. The objective of this paper is relevant since the current layout of the assembly line causes non-productive times for operators, being related to the lean waste of unnecessary movements. The methodology used for the project development was Project-based Learning (PBL), which is an active way of learning focused on real problems. The process of selecting the methodology for layout planning was developed considering three criteria to evaluate the most relevant one for this paper's goal. As a result of this evaluation, Systematic Layout Planning was selected, and three steps were added to it – Value Stream Mapping for the current situation and after layout changed and the definition of lean tools and layout type. This inclusion was to consider lean manufacturing in the layout redesign of the industry. The layout change resulted in an increase in the value-adding time of operations carried out in the sector, reduction in movement times between previous and final assemblies, and in cost savings regarding the man-hour value of the employees, which can be invested in productive hours instead of movement times.

Keywords: assembly line, layout, lean manufacturing, systematic layout planning

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Authors: Jacqueline Zammit

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Currently, a considerable number of individuals from different backgrounds are being drawn to Malta due to its favourable environment for business, investment, and employment. This influx has led to a growing interest among expats in learning Maltese as a second language (ML2) to enrich their experience of working and residing in Malta. However, the intricacies of Maltese grammar, particularly challenging for second language (L2) learners unfamiliar with Arabic, can pose difficulties in the learning process. Furthermore, it's worth noting that the teaching of ML2 is an emerging field with limited existing research on effective pedagogical strategies. The realm of second language acquisition (SLA) can be notably demanding for adults, requiring well-founded interventions to facilitate learning. Among these interventions, approaches grounded in empirical evidence have incorporated artistic and musical elements to augment SLA. Both art and music have proven roles in facilitating L2 communication, aiding vocabulary retention, and improving comprehension skills. This study aims to delve into the utilization of music and art as catalysts for enhancing the progress of adult learners in mastering ML2. The research employs a qualitative methodology, employing a sample selected through convenience sampling, which encompassed 37 adult learners of ML2. These participants engaged in individual interviews. The data derived from these interviews were subjected to thorough analysis. The outcomes of the study underscore the substantial positive influence exerted by art and music on the academic advancement of adult ML2 learners. Notably, it emerged from the participants' accounts that the current ML2 curricula lack the integration of art and music. Therefore, this study advocates for the incorporation of art and music components within both traditional classroom settings and online ML2 courses. The intention is to bolster the academic accomplishments of adult learners in the realm of Maltese as a second language, bridging the current gap between theory and practice.

Keywords: academic accomplishment, mature learners, visual art, learning Maltese as a second language, musical involvement, acquiring a second language

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate the benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. Developments improving the ease of ITS creation have recently increased their proliferation, leading many K-12 schools and institutions of higher education in the United States to regularly use ITS within classrooms. We investigated how students perceive their experience using an ITS. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course and were subsequently asked for feedback on their experience. Results show that their perceptions were generally favorable of the ITS, and most would seek to use an ITS both for STEM and non-STEM courses in the future. Along with detailed transaction-level data, this feedback also provides insights on the design of user-friendly interfaces, guidance on accessibility for students with impairments, the sequencing of exercises, students’ expectation of achievement, and comparisons to other tutoring experiences. We discuss how these findings are important for the creation, implementation, and evaluation of ITS as a mode and method of teaching and learning.

Keywords: college statistics course, intelligent tutoring systems, in vivo study, student perceptions of tutoring

Procedia PDF Downloads 104